(1) Field of the Invention
This invention relates generally to nonlinear dynamic systems and, in particular, to nonlinear oscillators.
(2) Description of the Prior Art
Various configurations of nonlinear mechanical and electrical devices have been employed to produce nonlinear dynamic systems. Examples of nonlinear oscillators include those known as the Bubble Oscillator, Duffing Oscillator, van der Pol Oscillator, the Toda Oscillator, and also the Fiegnbaum and Henon strange attractor algorithms. However, these oscillators or algorithms do not require neural networks for their implementation.
A large amount of literature exists in the field of artificial neural networks, or "neural nets". As one example, reference is made to Volumes 1 and 2 of "Parallel Distributed Processing-Explorations in the Microstructure of Cognition" by David E. Rumelhart, James E. McClelland and the PDP Research Group, The MIT Press, Cambridge, Mass. (1986). Reference is also made to U.S. Pat. No. 4,897,811, "N-Dimensional Coulomb Neural Network Which Provides For Cumulative Learning of Internal Representations", issued Jan. 30, 1990 to C. L. Scofield. This patent references a number of publications that describe various learning algorithms for multi-layer neural networks. Reference is also made to U.S. Pat. No. 4,748,674.
It is thus one object of this invention to provide a nonlinear oscillator that employs a neural network to provide a source of deterministic signals.
It is a further object of this invention to provide a nonlinear neural network oscillator that receives m-dimensional input vectors, outputs an n-dimensional vector, that includes a network for possibly modifying one or more output vector elements to provide a feedback signal, and wherein the feedback signal is stored so as to provide historical samples thereof for providing the m-dimensional input vector.